All Tools
T
OtherFreeOpen Source
TORCHAUDIO
Audio processing, datasets, and models for PyTorch
BSD-2-Clause
ABOUT
Audio and speech ML models require consistent preprocessing — resampling, spectrogram extraction, feature normalization, and I/O across dozens of audio formats — before they can be trained. TorchAudio standardizes these operations into composable transforms that integrate with PyTorch's data loading pipeline, supports hardware-accelerated audio decoding via SoX and FFmpeg backends, and provides pretrained models for speech recognition, source separation, and audio classification. Without it, practitioners manually wrangle format conversion and feature extraction for every audio dataset.
INSTALL
pip install torchaudioINTEGRATION GUIDE
1. Load and transform audio datasets for speech recognition and speaker diarization pipelines
2. Compute spectrograms, MFCCs, and other acoustic features for model training
3. Fine-tune pretrained audio models (Wav2Vec2, HuBERT, EnCodec) on custom tasks
4. Build end-to-end audio classification and sound event detection systems
TAGS
pytorchaudiospeech-recognitionsignal-processingdeep-learning